Over the years, we have developed several computational techniques for the statistical analysis of sets of electron micrographs of biological macromolecules. These methods include various types of factorial analyses (correspondence analysis, principal components), an outlier detection scheme, and clustering algorithms, as well as a statistical criterion for quantitative assessment of spatial resolution (spectral signal-to-noise ratio). An aspect that has been considered in more detail is finding the appropriate scaling parameters between two noisy images of similar particles when making high resolution 3-D reconstruction of icosahedral viruses from 2-D electron micrographs. The motivation is to be able to use a larger number of projective views by combining several micrographs in order to obtain higher resolution reconstructions. The problem is thus to correct for the different imaging conditions (scaling factors). For this purpose, we have derived a fast iterative algorithm that determines the scaling factors by matching auxiliary one-dimensional functions (radial autocorrelation) computed for each micrograph.